Design of a resistive brake controller for power system stability enhancement using reinforcement learning

Abstract-This paper considers a trajectory-based approach to determine control signals superimposed to those of existing controllers so as to enhance the damping of electromechanical oscillations. This approach is framed as a discrete-time, multi-step optimization problem which can be solved by model-based and/or by learning-based methods. This paper proposes to apply a model-free tree-based batch mode Reinforcement Learning (RL) algorithm to perform such a supplementary damping control based only on information collected from observed trajectories of the power system. This RL-based supplementary damping control scheme is first implemented on a single generator and then several possibilities are investigated for extending it to multiple generators. Simulations are carried out on a 16-generators medium size power system model, where also possible benefits of combining this RL-based control with Model Predictive Control (MPC) are assessed.

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“…This policy can be determined [9], [10] by computing the so-called action-value function (also called Q-function) defined by:…”

Section: Model-based Vs Model-free Solution Methodsmentioning

confidence: 99%

Trajectory-Based Supplementary Damping Control for Power System Electromechanical Oscillations

Wang

Glavić

Wehenkel

2014

IEEE Trans. Power Syst.

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“…If participating in these fast regulation schemes, EVs (if aggregated in enough number to provide this service) could be controlled as a dynamic brake using RL as demonstrated in Glavic (2005) and the same holds true for any other storage device mentioned earlier.…”

Section: Electric Vehicles and Storage: Challenges And Opportunitiesmentioning

confidence: 90%

“…Power system components considered include: dynamic brake Ernst et al (2004); Glavic (2005), thyristor controlled series capacitor Ernst et al (2004Ernst et al ( , 2009, quadrature booster Li and Wu (1999), synchronous generators (all AGC related references), individual or aggregated loads Vandael et al (2015); Ruelens et al (2016), etc. If used as a multi-agent system, then additional state variables must be introduced to ensure convergence of these essentially distributed computation schemes, and an adapted variant of standard RL methods is often used (for example correlated equilibrium Q(λ) Yu et al (2012a)).…”

Section: Past and Recent Considerations Of Rl For Electric Power Systmentioning

confidence: 99%

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

2017

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Abstract:In this paper, we review past (including very recent) research considerations in using reinforcement learning (RL) to solve electric power system decision and control problems. The RL considerations are reviewed in terms of specific electric power system problems, type of control and RL method used. We also provide observations about past considerations based on a comprehensive review of available publications. The review reveals the RL is considered as viable solutions to many decision and control problems across different time scales and electric power system states. Furthermore, we analyse the perspectives of RL approaches in light of the emergence of new-generation, communications, and instrumentation technologies currently in use, or available for future use, in power systems. The perspectives are also analysed in terms of recent breakthroughs in RL algorithms (Safe RL, Deep RL and path integral control for RL) and other, not previously considered, problems for RL considerations (most notably restorative, emergency controls together with so-called system integrity protection schemes, fusion with existing robust controls, and combining preventive and emergency control).

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“…The automotive antilock braking system has its own characteristic, besides the automobile itself environment difference request the system has the ability of the disturbance rejection and the redundant reliability, an important characteristic is the controlled process must be fast, this will limit the control algorithm, complex algorithm will be unable to realize it. Generally the control of such system has been on classical or PID feedback approaches [3], and the intent was to enhance the control by use of state space design and adaptive control [4,5,6], standard or linearization-based control design method have some drawbacks for such system, this is due to the lack of knowledge of the model and parameters.…”

Section: Introductionmentioning

confidence: 99%

The sliding mode controller for automotive ABS based on the fuzzy neural network

Mao

Zhang

Liu

2010

2010 Sixth International Conference on Natural Computation

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The sliding mode controller is presented for automotive Anti-lock Braking System (ABS), and the drawback of control chattering occurred in the classical sliding mode control can be alleviated with the proposed control scheme. Moreover, the robustness of neural network adaptive control system can be improved to some extent. Simulation research is performed to the vehicles brake on the wet road situation, and simulation results show the effectiveness and feasibility of the proposed scheme.

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Design of a resistive brake controller for power system stability enhancement using reinforcement learning

Cited by 29 publications

References 24 publications

Trajectory-Based Supplementary Damping Control for Power System Electromechanical Oscillations

Trajectory-Based Supplementary Damping Control for Power System Electromechanical Oscillations

Reinforcement Learning for Electric Power System Decision and Control: Past Considerations and Perspectives

The sliding mode controller for automotive ABS based on the fuzzy neural network

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